As already stated, most cnn-based object detectors use multiple (typically 3 or 5) prior boxes, or anchors, to encode their predictions. These anchors have different sizes and aspect ratios, and they are slid over the image to find candidates for the objects to localize. This method has two major drawbacks. First, it slows down training since, typically, there is the need for a very large set of anchors because the detector is trained to classify whether each anchor box sufficiently overlaps with a ground truth box, and a large number of anchor boxes is needed to ensure sufficient overlap with most ground truth boxes; as a result, only a tiny fraction of anchor boxes will overlap with ground truth, creating a huge imbalance between positive and negative anchor boxes [1]}. Second, it introduces many hyperparameters and design choices, including how many anchors, sizes, and aspect ratios should be employed. Such decisions are made mainly via ad-hoc heuristics and can become even more complicated when combined with multiscale architectures [2]}, [1]}, [4]}. Hence, recently, some works have explored anchorless detectors.
